
__lowerCAmelCase     :	int       			=  {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowerCAmelCase     :	Dict       			=  ['a', 'b', 'c', 'd', 'e']

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			start
  # add current to visited
  visited.append(A_   )
  __magic_name__								=			edges[current]
  for neighbor in neighbors:
    # if neighbor not in visited, visit
    if neighbor not in visited:
      __magic_name__								=			topological_sort(A_,	A_,	A_   )
    # if all neighbors visited add current to sort
  sort.append(A_   )
  # if all vertices haven't been visited select a new one to visit
  if len(A_   ) != len(A_   ):
    for vertice in vertices:
      if vertice not in visited:
        __magic_name__								=			topological_sort(A_,	A_,	A_   )
    # return sort
  return sort


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  topological_sort('a', [], [])
    print(sort)



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

